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Verifying the installation of profileR
Christopher David Desjardins edited this page Feb 13, 2020
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Thank you for your interest in the profileR package! If you have any suggestions or encounter any unexpected issues, please open an issue. We also warmly welcome contributions, so please feel free to submit a pull request.
There are two steps as a user of profileR that you can do to verify the functionality of profileR.
To check this, run the following two commands,
install.packages("profileR")
library(profileR)If you receive no output in the R Console, then profileR has installed correctly. It is possible, that a dependency has not installed correctly. If you get a warning or error message, please open an issue.
Below is the output from the examples, which can be used to verify that the program is properly functioning on your computer.
── Running 24 example files ─────────────────────────────────────────────────────────── profileR ──
> ### Name: cpa
> ### Title: Criterion-Related Profile Analysis
> ### Aliases: cpa
> ### Keywords: method
>
> ### ** Examples
>
>
> data(IPMMc)
> mod <- cpa(R ~ A + H + S + B, data = IPMMc)
> print(mod)
Call:
cpa(formula = R ~ A + H + S + B, data = IPMMc)
Coefficients
Call: glm(formula = formula, family = family, data = data, na.action = na.action)
Coefficients:
(Intercept) A H S B
0.500000 0.009231 0.023077 -0.009231 -0.023077
Degrees of Freedom: 5 Total (i.e. Null); 1 Residual
Null Deviance: 1.5
Residual Deviance: 0.04615 AIC: -0.1779
> summary(mod)
Call:
cpa(formula = R ~ A + H + S + B, data = IPMMc)
Relability
R2
Full Model 0.969231
Pattern 0.969231
Level 0.000000
Level Component
1 2 3 4 5 6
58.75 58.75 55.00 58.75 58.75 55.00
Pattern Component
A H S B
1 16.25 1.25 -8.75 -8.75
2 1.25 16.25 -13.75 -3.75
3 5.00 5.00 0.00 -10.00
4 -8.75 -8.75 16.25 1.25
5 -13.75 -3.75 1.25 16.25
6 0.00 -10.00 5.00 5.00
> plot(mod)
> anova(mod)
Call:
cpa(formula = R ~ A + H + S + B, data = IPMMc)
Analysis of Variance Table
df1 df2 F value Pr(>F)
R2.full = 0 4 1 7.87500e+00 0.2604188
R2.pat = 0 3 1 1.05000e+01 0.2221903
R2.lvl = 0 1 1 0.00000e+00 1.0000000
R2.full = R2.lvl 3 1 1.05000e+01 0.2221903
R2.full = R2.pat 1 1 -7.21645e-15 1.0000000
> ### Name: leisure
> ### Title: Leisure Activity Rankings
> ### Aliases: leisure
> ### Keywords: datasets
>
> ### ** Examples
>
>
> data(leisure)
> ### Name: mpa
> ### Title: Moderated Profile Analysis
> ### Aliases: mpa
> ### Keywords: method
>
> ### ** Examples
>
>
> data(mod_data)
> mod <- mpa(gpa ~ satv * major + satq * major, moderator = "major", data = bacc2001)
# -------- Executing Stage 1 -------- #
# -------- Executing Stage 2 -------- #
> summary(mod$output)
Call:
lm(formula = resp ~ 1 + level.ref + level.focal + pat.ref + pat.diff +
z, data = model.data)
Residuals:
Min 1Q Median 3Q Max
-143.511 -25.249 1.599 26.844 132.269
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 203.95852 10.18220 20.031 < 2e-16 ***
level.ref 0.21458 0.01786 12.014 < 2e-16 ***
level.focal -0.02860 0.02969 -0.963 0.336
pat.ref 2.00000 1.21898 1.641 0.101
pat.diff 2.00000 0.50062 3.995 6.91e-05 ***
z -3.56317 18.10308 -0.197 0.844
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 40.78 on 1074 degrees of freedom
Multiple R-squared: 0.1867, Adjusted R-squared: 0.1829
F-statistic: 49.3 on 5 and 1074 DF, p-value: < 2.2e-16
> mod$f.table
F.stat df1 df2 p-value
1.596045e+01 1.000000e+00 1.074000e+03 6.908838e-05
> summary(mod$moder.model)
Call:
lm(formula = formula, data = data, na.action = na.action)
Residuals:
Min 1Q Median 3Q Max
-143.511 -25.249 1.599 26.844 132.269
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 203.95852 10.18220 20.031 < 2e-16 ***
satv 0.13776 0.01851 7.444 2.0e-13 ***
majorstem -3.56317 18.10308 -0.197 0.844000
satq 0.07683 0.02251 3.414 0.000665 ***
satv:majorstem -0.12770 0.02849 -4.482 8.2e-06 ***
majorstem:satq 0.09910 0.03522 2.814 0.004984 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 40.78 on 1074 degrees of freedom
Multiple R-squared: 0.1867, Adjusted R-squared: 0.1829
F-statistic: 49.3 on 5 and 1074 DF, p-value: < 2.2e-16
> ### Name: pams
> ### Title: Profile Analysis via Multidimensional Scaling
> ### Aliases: pams
>
> ### ** Examples
>
>
> data(PS)
> result <- pams(PS[,2:4], dim=2)
> result
$weights.matrix
weight1 weight2 level R.sq
[1,] 1.5 0.00000 70 1
[2,] 1.5 0.00000 40 1
[3,] 0.0 2.12132 70 1
[4,] 0.0 2.12132 40 1
[5,] -1.5 0.00000 70 1
[6,] -1.5 0.00000 40 1
$dimensional.configuration
Dimension1 Dimension2
Neu -6.666667 -2.357023
Psy 0.000000 4.714045
CD 6.666667 -2.357023
> ### Name: paos
> ### Title: Profile Analysis for One Sample with Hotelling's T-Square
> ### Aliases: paos
>
> ### ** Examples
>
>
> data(nutrient)
> paos(nutrient, scale=TRUE)
T-Squared F df1 df2 p-value
Ho: Ratios of the means over Mu0=1 1392.347 276.9559 5 732 0
Ho: All of the ratios are equal to each other 1278.073 318.2159 4 733 0
> ### Name: pbg
> ### Title: Profile Analysis by Group: Testing Parallelism, Equal Levels,
> ### and Flatness
> ### Aliases: pbg
>
> ### ** Examples
>
>
> data(spouse)
> mod <- pbg(data=spouse[,1:4], group=spouse[,5], original.names=TRUE, profile.plot=TRUE)
> print(mod) #prints average scores in the profile across two groups
Data Summary:
Husband Wife
item1 3.900000 3.833333
item2 3.966667 4.100000
item3 4.333333 4.633333
item4 4.400000 4.533333
> summary(mod) #prints the results of three profile by group hypothesis tests
Call:
pbg(data = spouse[, 1:4], group = spouse[, 5], original.names = TRUE,
profile.plot = TRUE)
Hypothesis Tests:
$`Ho: Profiles are parallel`
Multivariate.Test Statistic Approx.F num.df den.df p.value
1 Wilks 0.8785726 2.579917 3 56 0.06255945
2 Pillai 0.1214274 2.579917 3 56 0.06255945
3 Hotelling-Lawley 0.1382099 2.579917 3 56 0.06255945
4 Roy 0.1382099 2.579917 3 56 0.06255945
$`Ho: Profiles have equal levels`
Df Sum Sq Mean Sq F value Pr(>F)
group 1 0.234 0.2344 1.533 0.221
Residuals 58 8.869 0.1529
$`Ho: Profiles are flat`
F df1 df2 p-value
1 8.18807 3 56 0.0001310162
> ### Name: pr
> ### Title: Pattern and Level Reliability via Profile Analysis
> ### Aliases: pr
> ### Keywords: methods
>
> ### ** Examples
>
>
> data(EEGS)
> result <- pr(EEGS[,c(1,3,5)],EEGS[,c(2,4,6)])
> print(result)
Subscore Reliability Estimates:
Estimate
Level 0.9245548
Pattern 0.9338338
Overall 0.9308374
> plot(result)
> ### Name: profileplot
> ### Title: Score Profile Plot
> ### Aliases: profileplot
>
> ### ** Examples
>
>
> data(PS)
> myplot <- profileplot(PS[,2:4], person.id = PS$Person,by.pattern = TRUE, original.names = TRUE)
> myplot
> data(leisure)
> leis.plot <- profileplot(leisure[,2:4],standardize=TRUE,by.pattern=FALSE)
> leis.plot
NULL
> ### Name: spouse
> ### Title: Love and Marriage Survey for Spouses
> ### Aliases: spouse
> ### Keywords: datasets
>
> ### ** Examples
>
>
> data(spouse)
> ### Name: wprifm
> ### Title: Within-Person Random Intercept Factor Model
> ### Aliases: wprifm
>
> ### ** Examples
>
> data <- HolzingerSwineford1939[,7:ncol(HolzingerSwineford1939)]
> wprifm(data, scale = TRUE)
lavaan 0.6-5 ended normally after 21 iterations
Estimator ML
Optimization method NLMINB
Number of free parameters 20
Number of equality constraints 1
Row rank of the constraints matrix 1
Number of observations 301
Model Test User Model:
Test statistic 158.922
Degrees of freedom 26
P-value (Chi-square) 0.000